Finding the molecular basis of quantitative traits
Genetics, Genomics, Population Genetics, Evolution, Computational Biology
The lab has moved and is now part of Initiative for Biological Systems Engineering, and Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai
In nature, most individuals vary by small degree, rather than descrete differences. For example, human height varies by degree not by presence or absence of height. Similarly, we see variation in skin and eye colour, susceptibility to several diseases such as diabetes, cancers, cardiac disorders. This kind of variation is brought about by multiple interacting genes and these characters are called quantitative traits. However, dissection of the genetic factors determining quantitative traits is difficult because of the multiple causal genes, each of which can contribute varying amounts to the character. Recent advances in molecular genetics and biology have provided methods to identify genes involved in these complex traits at a very high resolution. Yeast has become an excellent model system to study these traits at high resolution. We use yeast as a model to address some of the basic questions and fundamental genetic principles governing these traits and their role in evolution and phenotypic plasticity.
Gupta S, Radhakrishnan A, Nitin R, Pandu R-L, Lin G, Steinmetz LM, Gagneur J, Sinha H (2016) Meiotic interactors of a mitotic gene TAO3 revealed by functional analysis of its rare variant. G3: Genes, Genomes, Genetics (in press)
Gupta S, Radhakrishnan A, Pandu R-L, Lin G, Steinmetz LM, Gagneur J, Sinha H (2015) Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype. PLoS Genetics 11: e1005195
Yadav A, Radhakrishnan A, Bhanot G, Sinha H (2015) Differential regulation of antagonistic pleiotropy in synthetic and natural populations suggests its role in adaptation. G3: Genes, Genomes, Genetics 5: 699
Singh R, Sinha H (2015) Tiled RHS collection: a pilot high-throughput screening tool for identification of allelic variants. Yeast 32: 335.
Tomar P, Sinha H (2014) Conservation of PHO pathway in ascomycetes and role of Pho84. Journal of Biosciences 39: 1
Bhatia A, Yadav A, Zhu C, Gagneur J, Radhakrishnan A, Steinmetz L, Bhanot G, Sinha H (2014) Yeast growth plasticity is regulated by environment specific multi-QTL interactions. G3: Genes, Genomes, Genetics 4: 769
Tomar P, Bhatia A, Ramdas S, Diao L, Bhanot G, Sinha H (2013) Sporulation genes associated with sporulation efficiency in natural isolates of yeast. PLoS ONE 8: e69765
Fraser HB, Levy S, Chavan A, Shah HB, Perez JC, Zhou Y, Siegal ML, Sinha H (2012) Polygenic cis-regulatory adaptation in the evolution of yeast pathogenicity. Genome Research 22: 1930
Gagnuer J, Sinha H, Perocchi F, Bourgon R, Huber W, Steinmetz LM (2009) Genome-wide allele- and strand-specific expression profiling. Molecular Systems Biology 5: 274
Sinha H, David L, Pascon RC, Clauder-Muenster S, Krishnakumar S, Nguyen M, Shi G, Dean J, Davis RW, Oefner PJ, McCusker JH, Steinmetz LM (2008) Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast. Genetics 180: 1661
Sinha H, Nicholson BP, Steinmetz LM, McCusker JH (2006) Complex genetic interactions in a quantitative trait locus. PLoS Genetics 2: e13
Steinmetz LM, Sinha H, Richards DR, Spiegelman JI, Oefner PJ, McCusker JH, Davis RW (2002) Dissecting the architecture of a quantitative trait locus in yeast. Nature 416: 326